However, most of existing methods ignore the geometric information present in images.
Spatial re-ranking [1]} is usually used as a geometric filter to remove unrelated images from retrieval results. However, due to its expensive computation it is applied only to top ranked images for re-ranking. The spatial pyramid [2]} is a simple extension of the BOW representation which partitions the image into increasingly fine sub-regions and computes histograms of local features found inside each sub-region. It shows improved performance on scene classification tasks. The weak geometric consistency constraints (WGC) [3]} uses angle and scale information from key points to verify the consistency of matching descriptors. It can improve the retrieval performance significantly. Recently, Zhang et al. [4]} propose a technique to encode more spatial information through the geometry-preserving visual phrases (GVP) which requires a pair of images to obtain geometric information. Chum et al. [5]} propose geometric min-hash, which extends min-hash by adding local spatial extent to increase the discriminability of the descriptor. It can be used for nearly duplicate image detection but has not achieved the state-of-the art performance in retrieval.
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